code: def bottleneck_module(inputs, out_channel=256, res=None, scope=""):
      with tf.variable_scope(scope):
          net = slim.stack(
              inputs,
              slim.conv2d,
              [
                  (out_channel // 2, [1, 1]),
                  (out_channel // 2, [3, 3]),
                  (out_channel, [1, 1]),
              ],
              scope="conv",
          )
          if res:
              inputs = slim.conv2d(
                  inputs, res, (1, 1), scope="bn_res".format(scope)
              )
          net += inputs
  
          return net
  
file_path: insightface/reconstruction/ostec/external/landmark_detector/models.py
imports: import tensorflow
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: bottleneck_module(inputs, out_channel, res, scope) and returns: net